Interactive Data Visualization

Row

Honey Production

Honey Production Analysis

Column

Average State wise Production Map

Average Price per pound Map

Average Yield per colony Map

Average Stock by State Map

Overview

Row

State wise production graph

Price per pound graph

Yield Per Colony Graph

Stock by State Graph

---
title: "Varun's Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    social: ["twitter", "facebook", "menu"]
    source_code: embed
    theme: united
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
library(tidyverse)
library(crosstalk)
library(shiny)
require(plotly)
```

```{r}
df <- read.csv("honeyproduction.csv")
```




Interactive Data Visualization
========================================

Row
----------------------------------------

### Honey Production

```{r}
valueBox(paste("Honey Production Analysis"),
         color = "orange")
```


Column {.tabset .tabset-fade data-width=100 .colored}
-----------------------------------------------------------------------

### Average State wise Production Map


```{r}
map1 <- df %>%
         group_by(state) %>%
         summarize(total = mean(totalprod))

map1$state <- abbr2state(map1$state)

highchart() %>%
         hc_title(text = "Honey Production in the USA") %>%
         hc_subtitle(text = "Source: honeyproduction.csv") %>%
         hc_add_series_map(usgeojson, map1,
                           name = "state",
                           value = "total",
                           joinBy = c("woename", "state")) %>%
         hc_mapNavigation(enabled = T)


```


### Average Price per pound Map

```{r}
map2 <- df %>%
         group_by(state) %>%
         summarize(total = mean(priceperlb))

map2$state <- abbr2state(map2$state)

highchart() %>%
         hc_title(text = "Honey price perLb in the USA") %>%
         hc_subtitle(text = "Source: honeyproduction.csv") %>%
         hc_add_series_map(usgeojson, map2,
                           name = "state",
                           value = "total",
                           joinBy = c("woename", "state")) %>%
         hc_mapNavigation(enabled = T)

```

### Average Yield per colony Map

```{r}
map3 <- df %>%
         group_by(state) %>%
         summarize(total = mean(yieldpercol))

map3$state <- abbr2state(map3$state)

highchart() %>%
         hc_title(text = "Yield per Colony") %>%
         hc_subtitle(text = "Source: honeyproduction.csv") %>%
         hc_add_series_map(usgeojson, map3,
                           name = "state",
                           value = "total",
                           joinBy = c("woename", "state")) %>%
         hc_mapNavigation(enabled = T)

```

### Average Stock by State Map

```{r}
map4 <- df %>%
         group_by(state) %>%
         summarize(total = mean(stocks))

map4$state <- abbr2state(map4$state)

highchart() %>%
         hc_title(text = "Stocks per State by Colonies") %>%
         hc_subtitle(text = "Source: honeyproduction.csv") %>%
         hc_add_series_map(usgeojson, map3,
                           name = "state",
                           value = "total",
                           joinBy = c("woename", "state")) %>%
         hc_mapNavigation(enabled = T)

```


### Overview

```{r}
allPlots <- df %>% 
  group_by(year) %>% 
  mutate(
    colNum.year = mean(numcol),
    colYield.year = mean(yieldpercol),
    totalprod.year = mean(totalprod),
    totalStocks.year = mean(stocks),
    priceperlb.year = mean(priceperlb),
    totalProdValue.year = mean(prodvalue)) %>% 
  select(contains("year")) %>% 
  gather(key = "type", value = "value", -year)


label <- c(
  "colNum.year" = "No. of Honey colonies",
  "priceperlb.year" = "Average price per pound",
  "totalProdValue.year" = "Total production",
  "totalStocks.year" = "Total Stocks",
  "totalprod.year" = "Total production (pounds)",
  "colYield.year" = "Honey yield per colony"
)

plot1 <- allPlots %>% 
  ggplot(aes(x = year, y = value, group = type, color = type)) + 
  geom_line(show.legend = F) + 
  facet_wrap(~type, scales = "free_y", labeller = as_labeller(label), shrink = TRUE) + 
  geom_vline(xintercept = 2006, color = "red", 
             linetype = "dotted", size = 1.3) + 
  labs(y = "") 

plot1%>% ggplotly()



```


Row {.tabset .tabset-fade data-width=200 .colored }
-----------------------------------------------------------------------


### State wise production graph 

```{r}

state.production <- df %>% 
  ggplot(aes(x = year, y = totalprod/1000000, color = state)) + 
  geom_smooth(show.legend = T, se = FALSE) + 
  labs(title = "Honey Production from 1998 to 2012 by each state") + 
  ylab("Total Production in Millions")+
  xlab("Years ")
state.production %>% ggplotly()


```

### Price per pound graph

```{r}

pricePerPound <- df %>% 
  ggplot(aes(x = year, y = priceperlb, color = state)) + 
  geom_smooth(show.legend = T, se = FALSE) + 
  labs(title = "Honey Production from 1998 to 2012 by each state") + 
  ylab("Total Production in Millions")+
  xlab("Years ")
pricePerPound %>% ggplotly()

```

### Yield Per Colony Graph

```{r}

yieldPerColony <- df %>% 
  ggplot(aes(x = year, y = yieldpercol, color = state)) + 
  geom_smooth(show.legend = T, se = FALSE) + 
  labs(title = "Honey Production from 1998 to 2012 by each state") + 
  ylab("Total Production in Millions")+
  xlab("Years ")
yieldPerColony %>% ggplotly()

```

### Stock by State Graph

```{r}
p3 <- state.production <- df %>% 
  ggplot(aes(x = year, y = stocks, color = state)) + 
  geom_smooth(show.legend = T, se = FALSE) + 
  labs(title = "Honey Stock from 1998 to 2012 by each state") + 
  ylab("Stocks of Honey per State")+
  xlab("Years ")
state.production %>% ggplotly()

p3 %>% ggplotly()






```